sliding window Pattern
Pattern hubs are for building transferable solving frames. Learn the recognition signals first, then drill state definition, update rules, and edge explanation until the pattern feels stable.
Pattern brief
Recognize first
Do you recognize the need to update the start pointer when encountering duplicates?
Solve rhythm
State the active state and invariant first, explain how each update preserves them, then pressure-test with counterexamples.
Most common miss
Failing to move the left pointer past the previous duplicate, leading to incorrect max length calculations.
Recognition signals
- Do you recognize the need to update the start pointer when encountering duplicates?
- Can you explain why a hash map is used instead of nested loops for substring checks?
- Do you understand how to handle sliding window operations efficiently in this problem?
Solve flow
- 1. Define the active state/window.
- 2. Update state while preserving invariants.
- 3. Validate with edge-heavy examples.
Common misses
- Failing to move the left pointer past the previous duplicate, leading to incorrect max length calculations.
- Failing to update the hash table correctly when sliding the window, leading to incorrect word counts.
- Failing to account for duplicate characters in t, which can result in windows that appear valid but are missing counts.
Recommended Ladder
Problem bank
sliding window pattern bank
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